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Objective Bayesian nets from consistent datasets

Landes, Jürgen, Williamson, Jon (2016) Objective Bayesian nets from consistent datasets. In: BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 35TH INTERNATIONAL WORKSHOP ON BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. 1757. 020007. AIP ISBN 978-0-7354-1415-0. (doi:10.1063/1.4959048)

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Abstract

This paper addresses the problem of finding a Bayesian net representation of the probability function that agrees with the distributions of multiple consistent datasets and otherwise has maximum entropy. We give a general algorithm which is significantly more efficient than the standard brute-force approach. Furthermore, we show that in a wide range of cases such a Bayesian net can be obtained without solving any optimisation problem.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1063/1.4959048
Subjects: B Philosophy. Psychology. Religion > BC Logic
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Faculties > Humanities > School of European Culture and Languages > Philosophy
Depositing User: Jon Williamson
Date Deposited: 10 Aug 2016 08:42 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/56779 (The current URI for this page, for reference purposes)
Williamson, Jon: https://orcid.org/0000-0003-0514-4209
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